[1] Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]//Proceedings of the 28th Annual Conference on Neural Information Processing Systems, Montreal, Dec 7-12, 2015. Red Hook: Curran Associates, 2015: 91-99.
[2] Dai J F, Li Y, He K M, et al. R-FCN: object detection via region-based fully convolutional networks[C]//Proceedings of the 29th Annual Conference on Neural Information Processing Systems, Barcelona, Dec 5-10, 2016. Red Hook: Curran Asso-ciates, 2016: 379-387.
[3] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Jul 21-26, 2017. Piscataway: IEEE, 2017: 6517-6525.
[4] Chen X Z, Kundu K, Zhu Y K, et al. 3D object proposals for accurate object class detection[C]//Proceedings of the 28th Annual Conference on Neural Information Processing Systems, Montreal, Dec 7-12, 2015. Red Hook: Curran Associates, 2015: 424-432.
[5] Liu W, Anguelov D, Erhan D, et al. SSD: single shot multibox detector[C]//LNCS 9905: Proceedings of the 14th European Conference on Computer Vision, Amsterdam, Oct 11-14, 2016. Berlin, Heidelberg: Springer, 2016: 21-37.
[6] Zhu C C, He Y H, Savvides M. Feature selective anchor-free module for single-shot object detection[C]//Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, Jun 16-20, 2019. Piscataway: IEEE, 2019: 840-849.
[7] Li J Q, Liang X D, Wei Y C. Perceptual generative adversarial networks for small object detection[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Jul 21-26, 2017. Piscataway: IEEE, 2017: 1951-1959.
[8] Everingham M, Gool L, Williams C, et al. The pascal visual object classes (voc) challenge[J]. International Journal of Computer Vision, 2010, 88(2): 303-338.
[9] Lin T Y, Maire M, Belongie S, et al. Microsoft COCO: common objects in context[C]//LNCS 8693: Proceedings of the 2014 European Conference on Computer Vision, Zurich, Sep 6-12, 2014. Berlin, Heidelberg: Springer, 2014: 740-755.
[10] Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]//Proceedings of the 2005 IEEE Conference on Computer Vision and Pattern Recognition, San Diego, Jun 20-26, 2005. Piscataway: IEEE, 2005: 886-893.
[11] Shen Z Q, Liu Z, Li J G, et al. DSOD: learning deeply supervised object detectors from scratch[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision, Venice, Oct 22-29, 2017. Piscataway: IEEE, 2017: 1937-1945.
[12] Huang G, Liu Z, Maaten L, et al. Densely connected convolutional networks[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Jul 21-26, 2017. Piscataway: IEEE, 2017: 2261-2269.
[13] Cai Z W, Fan Q F, Feris R, et al. A unified multi-scale deep convolutional neural network for fast object detection[C]//LNCS 9908: Proceedings of the 2016 European Conference on Computer Vision, Amsterdam, Oct 11-14, 2016. Berlin, Heidelberg: Springer, 2016: 354-370.
[14] Liu S T, Huang D, Wang Y H. Receptive field block net for accurate and fast object detection[C]//LNCS 11215: Proc-eedings of the 2018 European Conference on Computer Vision, Munich, Sep 8-14, 2018. Berlin, Heidelberg: Springer, 2018: 404-419.
[15] Redmon J, Farhadi A. YOLOv3: an incremental improv-ment[J]. arXiv:1804.02767, 2018.
[16] Zhang S F, Wen L Y, Bian X, et al. Single-shot refinement neural network for object detection[C]//Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, Jun 18-22, 2018. Piscataway: IEEE, 2018: 4203-4212.
[17] Lin T, Dollár P, Girshick R, et al. Feature pyramid networks for object detection[C]//Proceedings of the 2017 IEEE Con-ference on Computer Vision and Pattern Recognition, Honolulu, Jul 21-26, 2017. Piscataway: IEEE, 2017: 936-944.
[18] Zhou P, Ni B B, Geng C, et al. Scale-transferrable object detection[C]//Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, Jun 18-22, 2018. Piscataway: IEEE, 2018: 528-537.
[19] Fu C Y, Liu W, Ranga A, et al. DSSD: deconvolutional single shot detector[J]. arXiv:1701.06659, 2017.
[20] Cui L S, Ma R, Lv P, et al. MDSSD: multi-scale deconv-olutional single shot detector for small objects[J]. arXiv: 1805.07009, 2018.
[21] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, Jun 18-22, 2018. Piscataway: IEEE, 2018: 7132-7141.
[22] Kong T, Sun F C, Hua W B, et al. Deep feature pyramid reconfiguration for object detection[C]//LNCS 11209: Proc-eedings of the 2018 European Conference on Computer Vision, Munich, Sep 8-14, 2018. Berlin, Heidelberg: Springer, 2018: 172-188.
[23] Wang X L, Girshick R, Gupta A, et al. Non-local neural networks[C]//Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, Jun 18-22, 2018. Piscataway: IEEE, 2018: 7794-7803. |